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The Automatic Multi-Class Classification of Hate Speech on Twitter.

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Automatic Multi-Class Classification of Hate Speech on Twitter./
Author:	Razzaghi, Masoumeh.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	99 p.
Notes:	Source: Masters Abstracts International, Volume: 83-07.
Contained By:	Masters Abstracts International83-07.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28861032
ISBN:	9798762198745

The Automatic Multi-Class Classification of Hate Speech on Twitter.
Razzaghi, Masoumeh.

The Automatic Multi-Class Classification of Hate Speech on Twitter. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 99 p.

Source: Masters Abstracts International, Volume: 83-07.

Thesis (M.S.)--Texas A&M University - Commerce, 2021.

This item must not be sold to any third party vendors.

The spread of hate speech on social media is becoming a significant concern. To address this concern, various scholars from diverse disciplines such as sociology, legal studies and computer science have attempted to define, analyze and detect hate speech. However, hate speech has not been adequately addressed and analyzed as a sociolinguistic phenomenon. Therefore, the aim of this study is to shed more light on understanding hate speech as a sociolinguistic concept. To achieve this goal, three main phases have been performed. First, the study incorporates the theory of speech acts along with the existing academic and non-academic definitions of hate speech along to propose a more comprehensive definition. Using the new definition, the study proposed a fine-grained taxonomy of hate speech. In addition, the study proposed the main components of hate speech which can distinguish this concept from the general profanity. In the next phase, two hate speech datasets are created and an annotation scheme was developed based on the proposed taxonomy. Finally, using the annotated hate speech dataset, several multi-class, multi-label classification of hate speech are conducted to investigate the impact of the new annotation framework.

ISBN: 9798762198745Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Sociolinguistics

The Automatic Multi-Class Classification of Hate Speech on Twitter.
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245 1 4 $a The Automatic Multi-Class Classification of Hate Speech on Twitter.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 99 p.
500 $a Source: Masters Abstracts International, Volume: 83-07.
500 $a Advisor: EL Ariss, Omar.
502 $a Thesis (M.S.)--Texas A&M University - Commerce, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The spread of hate speech on social media is becoming a significant concern. To address this concern, various scholars from diverse disciplines such as sociology, legal studies and computer science have attempted to define, analyze and detect hate speech. However, hate speech has not been adequately addressed and analyzed as a sociolinguistic phenomenon. Therefore, the aim of this study is to shed more light on understanding hate speech as a sociolinguistic concept. To achieve this goal, three main phases have been performed. First, the study incorporates the theory of speech acts along with the existing academic and non-academic definitions of hate speech along to propose a more comprehensive definition. Using the new definition, the study proposed a fine-grained taxonomy of hate speech. In addition, the study proposed the main components of hate speech which can distinguish this concept from the general profanity. In the next phase, two hate speech datasets are created and an annotation scheme was developed based on the proposed taxonomy. Finally, using the annotated hate speech dataset, several multi-class, multi-label classification of hate speech are conducted to investigate the impact of the new annotation framework.
590 $a School code: 1287.
650 4 $a Computer science. $3 523869
650 4 $a Web studies. $3 2122754
650 4 $a Sociolinguistics. $3 524467
653 $a Sociolinguistics
653 $a Speech act theory
653 $a Multi-class
653 $a Multi-label classification
690 $a 0984
690 $a 0646
690 $a 0636
710 2 $a Texas A&M University - Commerce. $b MS-Computer Science. $3 2092429
773 0 $t Masters Abstracts International $g 83-07.
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791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28861032